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Abstract

Introduction

Health care utilisation ('claims') databases contain information about millions of
patients and are an important source of information for a variety of study types.
However, they typically do not contain information about disease severity. The goal
of the present study was to develop a health care claims index for rheumatoid arthritis
(RA) severity using a previously developed medical records-based index for RA severity
(RA medical records-based index of severity [RARBIS]).

Methods

The study population consisted of 120 patients from the Veteran's Administration (VA)
Health System. We previously demonstrated the construct validity of the RARBIS and
established its convergent validity with the Disease Activity Score (DAS28). Potential
claims-based indicators were entered into a linear regression model as independent
variables and the RARBIS as the dependent variable. The claims-based index for RA
severity (CIRAS) was created using the coefficients from models with the highest coefficient
of determination (R2) values selected by automated modelling procedures. To compare our claims-based index
with our medical records-based index, we examined the correlation between the CIRAS
and the RARBIS using Spearman non-parametric tests.

Results

The forward selection models yielded the highest model R2 for both the RARBIS with medications (R2 = 0.31) and the RARBIS without medications (R2 = 0.26). Components of the CIRAS included tests for inflammatory markers, number of
chemistry panels and platelet counts ordered, rheumatoid factor, the number of rehabilitation
and rheumatology visits, and Felty's syndrome diagnosis. The CIRAS demonstrated moderate
correlations with the RARBIS with medication and the RARBIS without medication sub-scales.

Conclusion

We developed the CIRAS that showed moderate correlations with a previously validated
records-based index of severity. The CIRAS may serve as a potentially important tool
in adjusting for RA severity in pharmacoepidemiology studies of RA treatment and complications
using health care utilisation data.

Introduction

Rheumatoid arthritis (RA) is an autoimmune disease characterised by pain, morning
stiffness, joint swelling, deformity and functional impairments. Patients with RA
have an increased risk of mortality and several adverse outcomes such as infections
and cancer compared with those who do not have RA [1-4]. Several studies, however, suggest that complications in RA patients may not be attributable
to the disease itself, but to the use of disease-modifying anti-rheumatic drugs (DMARD).
For instance, tumour necrosis factor (TNF) α blocking agents have an association with
specific types of infections and may be related to an excess risk of lymphomas and
neurological complications [5-9]. Conventional DMARDs may also increase the incidence of lymphoma [10,11].

In studies that seek to determine the relationship between drug therapy and adverse
events, disease severity is an important confounder. That is, disease severity is
known to increase the risk of many adverse events and is probably associated with
a higher likelihood of receiving more immunomodulating DMARDs. Failure to adjust for
such confounding by indication can create false associations between the exposure
and study outcome [12].

Health care utilisation ('claims') data are routinely collected for insurance and
are commonly used in health services research [13,14]. Because adverse outcomes of RA are relatively rare, health care utilisation databases
are an ideal source of information for studies of the relationship between DMARDs
and adverse events such as cancer and infections. Thus, the development of an RA disease
severity measure from claims merits high priority. We believe that health care claims
data contain information such as physician visits, surgeries and laboratory tests
that correlate with RA disease severity. Thus, to develop a claims-based severity
index, we first created an RA medical records-based index of severity (RARBIS) from
ratings by a Delphi panel on potential markers of RA severity commonly found in medical
charts [15]. We then assessed the performance of the RARBIS in a cohort of Veteran's Administration
(VA) patients and showed that the RARBIS correlated moderately well with RA treatment
intensity and thus exhibited construct validity [16]. Next, we established the convergent validity of the RARBIS against a widely-used
and accepted RA clinical measure, the Disease Activity Score (DAS28) [17]. The goal of the present study was to develop a claims-based severity index (claims-based
index for RA severity [CIRAS]) using the previously validated RARBIS, not the DAS28.
If validated as a measure of RA disease severity, the CIRAS may serve as a potentially
important tool in adjusting for RA severity in pharmacoepidemiology studies of RA
treatment and complications using health care utilisation data.

Materials and methods

Study population and data source

The study population consisted of 120 patients from the New England region of the
VA Health System who had at least two recorded visits with a diagnosis of RA (International
Classification of Disease-9-CM 714.0), at least two outpatient visits from hospitals
within the New England VA Health System from July 1999 to June 2001 and had sufficient
evidence of RA from their medical record. The VA maintains a comprehensive electronic
medical records database containing information on demographic characteristics, surgical
history, prescriptions, laboratory results, discharge summaries, radiology reports
and progress notes. A review of the VA electronic medical records of the study population
was conducted to obtain information on individual components of the RARBIS. The current
study was approved by the VA Health System Human Subjects Committee.

RA records-based index of severity

A records-based index of severity was developed based on ratings from a Delphi panel
of six New England board certified rheumatologists of potential indicators of RA severity
[15]. The potential indicators were divided into the following categories: radiological
and laboratory results; surgeries; extra-articular manifestations; clinical and functional
status; and medications (see Table 1). Indicators that were ranked by the panel as having strong or very strong associations
with RA severity and are typically found in medical charts were incorporated into
the RARBIS. Sub-scales and individual components of the RARBIS were weighted according
to how strongly they were regarded by the panel as being correlated with disease severity.
Because we wanted to develop an administrative-based severity score that could be
used to study drug-outcome relationships, we created the RARBIS with the option to
exclude the medication sub-scale.

Each physical therapy and occupational therapy visit was counted as a rehabilitation
visit. Tests for CRP and ESR were aggregated into one category. Tests performed on
the same day counted as separate tests. The number of hand, wrist, foot, ankle and
cervical spine radiographs were also added together into one category. Three methods
were used to count the number of prescriptions in a given year. First, we counted
the total number of prescriptions (including repeat prescriptions) for the following
10 medications: auranofin, aurothioglucose, azathioprine, cyclosporine, etanercept
(Enbrel, Amgen), hydroxychloroquine, infliximab (Remicade, Centocor), leflunomide,
methotrexate and sulfasalazine (adalimumab, abatacept and rituximab were not yet available
for RA). For the second method, prescriptions for each DMARD were counted once and
added to obtain the total number of different DMARDs. For the third method, synthetic
DMARDs and biological DMARDs were counted separately. Prescription for each type of
DMARD was counted only once and then added together to obtain the total number of
different synthetic DMARDs and biological DMARDs.

Statistical analyses

For each patient, scores were calculated for the RARBIS with and without the medication
sub-scale using data from the medical chart review. Using Spearman non-parametric
tests, the correlations between the RARBIS and various forms of administrative data
variables were then analysed. Data taken from one year before the chart review and
from two years before the chart review were examined.

We then built linear regression models with the RARBIS as the dependent variable and
the administrative data variables as the independent variables using SAS (Cary NC)
automated procedures and the forward, backward and stepwise selection methods to select
the best model. Administrative data variables were entered into the model in the form
that gave the highest Spearman correlation with the RARBIS. The inclusion criterion
for model selection was p < 0.2.

We added the regression parameters based on each patient's covariate values using
PROC Score (SAS, Cary NC) to calculate claims-based severity scores (with and without
the medication variables) for each patient in the study cohort. Finally, we examined
the correlation between the CIRAS and the RARBIS using the non-parametric Spearman
correlation coefficient.

Results

Characteristics of the study population are summarised in Table 2. The study cohort was predominantly male with a mean age of 71 years. During the
chart review study period, most had no functional limitations (78%) and did not require
a device or wheelchair for ambulatory purposes (66%). About one-half of the population
had swollen joints, morning stiffness that lasted less than one hour, but did not
have an arthritis flare. The mean score for the RARBIS with medications was 4.4 (range
0 to 11) and without medications was 3.0 (range 0 to 8).

Table 2. Patient characteristics based on information from the medical records review

Table 3 provides the unadjusted Spearman correlations for the claims-based RA severity variables
and the RARBIS with and without the medication sub-scale using data from one year
before the chart review study period. The variables for rheumatology visits, inflammatory
markers and other laboratory markers yielded the highest correlation with the RARBIS.
In our analysis using administrative data from one year before the chart review period,
the highest correlation between the RARBIS and the medication variable were obtained
using the medication variable created from the sum of all DMARD prescriptions in method
one. For both the RARBIS with and without medication scale, having data from two years
before the chart review period did not substantially increase the Spearman correlation
coefficients and, in some cases, even decreased the value of the coefficients (data
not shown).

Table 3. Unadjusted Spearman correlations with the rheumatoid arthritis records-based index
of severity (RARBIS) with and without medication sub-scale

Table 4 presents the adjusted correlations between the claims-based RA severity variables
and the RARBIS with and without the medication sub-scale with data from one year before
the chart review study period. The forward selection models yielded the highest model
R2 for both the RARBIS with the medication sub-scale (R2 = 0.31) and the RARBIS without the medication sub-scale (R2 = 0.26). Using two years of data resulted in lower model R2s (data not shown).

Table 4. Adjusted correlations between claims-based variables and rheumatoid arthritis records-based
index of severity (RARBIS) with and without medication sub-scale

Table 5 includes the means and ranges for the CIRAS scores and the Spearman correlation coefficients
between the CIRAS and the RARBIS. The CIRAS score with the highest correlation with
the RARBIS included the following components: orders for inflammatory markers, rehabilitation
visits, age and gender, rheumatoid factor, presence of Felty's syndrome, number of
platelet counts and chemistry panels ordered, and rheumatology visits. Figure 1 is a graphic representation of this CIRAS score in tertiles versus the median and
interquartile range for the RARBIS with medication sub-scale. Table 6 presents the suggested scoring method for the CIRAS.

Figure 1. This plot illustrates the median and interquartile range for the tertiles of the claims-based
index of rheumatoid arthritis severity (CIRAS). These values are plotted against the
records-based index of rheumatoid arthritis severity (RARBIS). The CIRAS exhibits
a moderate linear correlation with the RARBIS.

Discussion

We developed a claims-based RA severity index (CIRAS) that demonstrated moderate correlation
with a previously validated medical records-based index, the RARBIS. The RARBIS has
been previously shown to have good construct validity and moderate convergent validity
with the DAS28 [16,17]. Because health care utilisation databases are a valuable source of data for studying
health outcomes, other investigators have also used medical records-based indices
to create indices for administrative databases. For instance, Deyo and colleagues
adapted the Charlson Comorbidity Index, a well-validated index designed for medical
records, so that International Classification of Diseases, Ninth Revision codes could be used to calculate the Charlson Comorbidity Index from administrative
data [18]. Components of the administrative-based index we developed for RA include orders
for inflammatory markers, number of platelet counts and chemistry panels ordered,
rheumatoid factor, rehabilitation visits, age and gender, presence of Felty's syndrome
and number of rheumatology visits. If the CIRAS is found to be valid in other populations,
then it might be used to partially adjust for an important confounder, disease severity,
in claims-based epidemiology studies. In our analysis, we used data taken from one
and two years before the chart review study period. However, using two years of data
resulted in lower R2 and Spearman correlation values. Including another year of older data might have caused
a dilution effect. Additionally, to compute scores on the CIRAS, we used weights from
the regression models with the RARBIS. Other methods of weighting could have been
chosen, for example, assigning a value of one to administrative variables that had
significant correlations with the RARBIS. However, we believe that the method we selected,
using beta coefficients as weights, better captures the relationship between the CIRAS
and the RARBIS.

Because administrative data are collected primarily for reimbursement purposes, some
question the use of claims data for clinical research regarding disease severity [19]. However, administrative data are gaining increasing acceptance in health care research,
because they represent typical populations, contain large cohorts of patients with
given conditions and are readily available. We also demonstrate in the present study
that indicators of RA severity from claims data are moderately well related to clinical
indicators of RA severity. Thus, it is possible to capture RA disease severity to
some degree in claims data. Other proxies for severity of illness measures using claims
data such as the diagnosis related group, the all patients refined diagnosis related
group and the International Classification of Diseases Ninth Revision-Based Illness Severity Score have been developed [20]. Unlike the CIRAS, these other measures are not specific to RA.

The present study has important limitations. Our data source for this study was the
New England VA Health system. The VA's population is mostly older men. Older male
patients with RA may not represent typical RA patients. This highlights the need to
consider these findings as preliminary and requiring replication in other settings.
Additionally, data from the VA might be gathered differently from other health care
systems, again highlighting the preliminary nature of our findings. However, because
the VA contains rich data from both medical record and health care utilisation databases,
it is a unique and ideal data source for our analysis. Additionally, the RARBIS, which
we used to create the CIRAS, was developed using standard nominal group technique
methods, followed by assessing its convergent validity with the DAS28. However, the
DAS28 is a measure of disease activity not disease severity. While disease activity
is an important component of disease severity, it is not the same. Currently, there
is no standard RA disease severity measure.

In our cohort of 120 VA patients, the CIRAS showed moderate correlations with a validated
medical records-based index and can be used for improved adjustment of RA disease
severity in claims data studies. We do not believe that the value of the CIRAS will
be limited to the VA population. We plan on assessing its validity in other populations,
such as Medicare patients, and will examine its ability to adjust for confounding
and predictive validity for outcomes known to be associated with severe RA, such as
future joint surgeries, higher medical care costs and use of combination DMARDs. Additionally,
we will explore whether different variations of the CIRAS should be used depending
on the study outcome of interest. Ultimately, the CIRAS may be an important methodological
tool for researchers studying RA treatment and complications using health care utilisation
data, but further tests need to be conducted in other populations.

Conclusion

We developed a claims-based severity index (CIRAS) from a previously validated medical
records-based index (RARBIS). The CIRAS can potentially be used for improved adjustment
of RA severity in studies of RA medication use and adverse outcomes using claims data,
but future studies should examine its validity in other populations.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

GT analysed the data and drafted the manuscript. SS provided support on the statistical
analyses, interpretation of data and helped edit the manuscript. RS provided access
to the data and helped edit the manuscript. JNK and MEW provided advice on the conceptual
design and helped edit the manuscript. MY provided access to the data and helped edit
the manuscript. JA contributed conceptual advice and helped edit the manuscript. DHS
provided conceptual design, analytic support, access to the data and helped edit the
manuscript.

Acknowledgements

This work was supported by the Engalitcheff Arthritis Outcomes Initiative. Dr. Solomon's
work is also supported by National Institute of Health grants (P60 AR47782 and K24
AR055989). Dr Katz's work is supported by National Institute of Health grants (P60
AR47782 and K24 AR02123). This material is the result of work supported with resources
and the use of facilities at the VA Boston Healthcare System and VA Cooperative Studies
Program.